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https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
104 lines
3.8 KiB
Python
104 lines
3.8 KiB
Python
import torch
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def get_ltxv_text_encoder_filename(text_encoder_quantization):
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text_encoder_filename = "ckpts/T5_xxl_1.1/T5_xxl_1.1_enc_bf16.safetensors"
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if text_encoder_quantization =="int8":
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text_encoder_filename = text_encoder_filename.replace("bf16", "quanto_bf16_int8")
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return text_encoder_filename
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class family_handler():
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@staticmethod
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def query_model_def(base_model_type, model_def):
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flux_model = model_def.get("flux-model", "flux-dev")
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flux_schnell = flux_model == "flux-schnell"
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model_def_output = {
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"image_outputs" : True,
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"no_negative_prompt" : True,
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}
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if flux_schnell:
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model_def_output["no_guidance"] = True
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else:
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model_def_output["embedded_guidance"] = True
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return model_def_output
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@staticmethod
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def query_supported_types():
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return ["flux"]
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@staticmethod
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def query_family_maps():
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return {}, {}
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@staticmethod
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def get_rgb_factors(model_type):
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from shared.RGB_factors import get_rgb_factors
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latent_rgb_factors, latent_rgb_factors_bias = get_rgb_factors("flux")
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return latent_rgb_factors, latent_rgb_factors_bias
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@staticmethod
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def query_model_family():
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return "flux"
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@staticmethod
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def query_family_infos():
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return {"flux":(30, "Flux 1")}
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@staticmethod
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def query_model_files(computeList, base_model_type, model_filename, text_encoder_quantization):
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text_encoder_filename = get_ltxv_text_encoder_filename(text_encoder_quantization)
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return [
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{
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"repoId" : "DeepBeepMeep/Flux",
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"sourceFolderList" : [""],
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"fileList" : [ ["flux_vae.safetensors"] ]
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},
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{
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"repoId" : "DeepBeepMeep/LTX_Video",
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"sourceFolderList" : ["T5_xxl_1.1"],
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"fileList" : [ ["added_tokens.json", "special_tokens_map.json", "spiece.model", "tokenizer_config.json"] + computeList(text_encoder_filename) ]
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},
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{
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"repoId" : "DeepBeepMeep/HunyuanVideo",
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"sourceFolderList" : [ "clip_vit_large_patch14", ],
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"fileList" :[
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["config.json", "merges.txt", "model.safetensors", "preprocessor_config.json", "special_tokens_map.json", "tokenizer.json", "tokenizer_config.json", "vocab.json"],
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]
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}
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]
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@staticmethod
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def load_model(model_filename, model_type, base_model_type, model_def, quantizeTransformer = False, text_encoder_quantization = None, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False, save_quantized = False):
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from .flux_main import model_factory
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flux_model = model_factory(
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checkpoint_dir="ckpts",
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model_filename=model_filename,
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model_type = model_type,
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model_def = model_def,
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base_model_type=base_model_type,
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text_encoder_filename= get_ltxv_text_encoder_filename(text_encoder_quantization),
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quantizeTransformer = quantizeTransformer,
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dtype = dtype,
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VAE_dtype = VAE_dtype,
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mixed_precision_transformer = mixed_precision_transformer,
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save_quantized = save_quantized
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)
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pipe = { "transformer": flux_model.model, "vae" : flux_model.vae, "text_encoder" : flux_model.clip, "text_encoder_2" : flux_model.t5}
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return flux_model, pipe
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@staticmethod
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def update_default_settings(base_model_type, model_def, ui_defaults):
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ui_defaults.update({
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"embedded_guidance": 2.5,
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})
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if model_def.get("reference_image", False):
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ui_defaults.update({
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"video_prompt_type": "KI",
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})
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